Chapter 8 Differential abundance analysis
8.6 Community level plots
accli_post2<-sample_metadata%>%
filter(time_point == "1_Acclimation"|time_point == "6_Post-FMT2")
accli_post2$newID<-paste(accli_post2$type, "_", accli_post2$time_point)
GIFTs_functions_community %>%
as.data.frame() %>%
rownames_to_column("sample") %>%
left_join(accli_post2, by = join_by(sample == Tube_code)) %>%
filter(time_point == "1_Acclimation"|time_point == "6_Post-FMT2") %>%
select(c(1:21, 27,30)) %>%
pivot_longer(-c(sample,type,time_point),names_to = "trait", values_to = "value") %>%
mutate(trait = case_when(
trait %in% GIFT_db$Code_function ~ GIFT_db$Function[match(trait, GIFT_db$Code_function)],
TRUE ~ trait
)) %>%
mutate(trait=factor(trait,levels=unique(GIFT_db$Function))) %>%
ggplot(aes(x=value, y=time_point, group=time_point, fill=type, color=type)) +
geom_boxplot() +
scale_color_manual(name="type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
scale_fill_manual(name="type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
facet_grid(trait ~ type, space="free", scales="free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
strip.text.y = element_text(angle = 0)) +
labs(y="Traits",x="Metabolic capacity index")
GIFTs_elements_community_merged<-GIFTs_elements_community %>%
as.data.frame() %>%
rownames_to_column(var="sample") %>%
filter(sample!="AD69") %>%
pivot_longer(!sample,names_to="trait",values_to="gift") %>%
left_join(sample_metadata, by = join_by(sample == Tube_code))%>%
filter(time_point=="1_Acclimation"|time_point == "6_Post-FMT2")%>%
mutate(functionid = substr(trait, 1, 3)) %>%
mutate(trait = case_when(
trait %in% GIFT_db$Code_element ~ GIFT_db$Element[match(trait, GIFT_db$Code_element)],
TRUE ~ trait
)) %>%
mutate(functionid = case_when(
functionid %in% GIFT_db$Code_function ~ GIFT_db$Function[match(functionid, GIFT_db$Code_function)],
TRUE ~ functionid
)) %>%
mutate(trait=factor(trait,levels=unique(GIFT_db$Element))) %>%
mutate(functionid=factor(functionid,levels=unique(GIFT_db$Function)))
# Create an interaction variable for time_point and sample
GIFTs_elements_community_merged$interaction_var <- interaction(GIFTs_elements_community_merged$sample, GIFTs_elements_community_merged$time_point)
ggplot(GIFTs_elements_community_merged,aes(x=interaction_var,y=trait,fill=gift)) +
geom_tile(colour="white", linewidth=0.2)+
scale_fill_gradientn(colours=rev(c("#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#ddf1da")))+
facet_grid(functionid ~ type, scales="free",space="free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1, size=5),
strip.text.y = element_text(angle = 0)) +
labs(y="Traits",x="Time_point",fill="GIFT")+
scale_x_discrete(labels = function(x) gsub(".*\\.", "", x))
8.7 Wilcoxon comparison
8.7.1 Community elements differences: in CC acclimation vs post2
[1] Elements Acclimation Post2 Function Element Difference group_color
<0 rows> (or 0-length row.names)
8.7.2 Community elements differences: in CI acclimation vs post2
difference_table_CI %>%
filter(group_color=="Acclimation")
8.7.3 Community elements differences: in WC acclimation vs post2
difference_table_WC %>%
filter(group_color=="Acclimation")
8.7.4 Comparison of both population in wild samples
Elements Acclimation Post2 Function Element Difference group_color
1 D0301 0.03445823 0.02175758 Sugar degradation_Lactose Lactose 0.01270065 Acclimation
2 B0711 0.31689890 0.23431920 Vitamin biosynthesis_Menaquinone (K2) Menaquinone (K2) 0.08257970 Acclimation
####Butiryc acid biosynthesis
